1173 lines
29 KiB
C++
1173 lines
29 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Templates exposing architecture support for multiply-add operations
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/gemm/thread/mma.h"
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#include "cutlass/functional.h"
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#include "cutlass/reduction/thread/reduce.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace thread {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace detail {
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/// Structure to compute the matrix product for HFMA
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template <
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/// Size of the Gemm problem - concept: gemm::GemmShape<>
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typename Shape,
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/// Layout of A matrix (concept: MatrixLayout)
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typename LayoutA,
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/// Layout of B matrix (concept: MatrixLayout)
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typename LayoutB,
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/// Layout of C matrix (concept: MatrixLayout)
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typename LayoutC,
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/// Type of GEMM inner vs outer product
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bool
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>
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struct Mma_HFMA2;
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/////////////////////////////
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// Specialization for NNN //
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/////////////////////////////
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template <typename Shape_>
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struct Mma_HFMA2 <
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Shape_,
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layout::ColumnMajor,
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layout::ColumnMajor,
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layout::ColumnMajor,
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true
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> {
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/// Size of the Gemm problem - concept: gemm::GemmShape<>
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using Shape = Shape_;
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/// A operand storage
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using FragmentA = Array<half_t, Shape::kMK>;
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/// B operand storage
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using FragmentB = Array<half_t, Shape::kKN>;
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/// C operand storage
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using FragmentC = Array<half_t, Shape::kMN>;
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/// Underlying mathematical operator
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using Operator = arch::OpMultiplyAdd;
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static_assert(
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!(Shape::kM % 2),
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"Mma_HFMA2 requires the M dimension to be divisible by 2."
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);
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//
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// Methods
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//
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/// Computes a matrix product D = A * B + C
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CUTLASS_HOST_DEVICE
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void operator()(
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FragmentC & D,
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FragmentA const & A,
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FragmentB const & B,
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FragmentC const & C) {
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/// Initialize output with input
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D = C;
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/// Use 1x1x1 HFMA2 sequence for bulk of computation
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using Mma = arch::Mma<
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gemm::GemmShape<2,1,1>,
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1,
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half_t,
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layout::ColumnMajor,
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half_t,
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layout::ColumnMajor,
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half_t,
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layout::ColumnMajor,
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arch::OpMultiplyAdd>;
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Array<half_t, 2> *ptr_D = reinterpret_cast<Array<half_t, 2> *>(&D);
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Array<half_t, 2> const *ptr_A = reinterpret_cast<Array<half_t, 2> const *>(&A);
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Array<half_t, 1> const *ptr_B = reinterpret_cast<Array<half_t, 1> const *>(&B);
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Mma mma;
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CUTLASS_PRAGMA_UNROLL
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for(auto k=0; k < Shape::kK / Mma::Shape::kK; k++){
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CUTLASS_PRAGMA_UNROLL
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for(auto m=0; m < Shape::kM / Mma::Shape::kM; m++){
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CUTLASS_PRAGMA_UNROLL
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for(auto n=0; n < Shape::kN / Mma::Shape::kN; n++){
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Array<half_t, 2> tmp;
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Array<half_t, 2> *ptr_tmp = &tmp;
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ptr_tmp[0] = ptr_D[n*Shape::kM/2 + m];
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mma(
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tmp,
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ptr_A[k*Shape::kM/2 + m],
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ptr_B[n*Shape::kK + k],
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tmp);
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ptr_D[n*Shape::kM/2 + m] = ptr_tmp[0];
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}
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}
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}
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}
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};
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/////////////////////////////
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// Specialization for NNT //
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/////////////////////////////
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template <typename Shape_>
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struct Mma_HFMA2<
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Shape_,
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layout::ColumnMajor,
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layout::ColumnMajor,
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layout::RowMajor,
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true
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> {
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/// Size of the Gemm problem - concept: gemm::GemmShape<>
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using Shape = Shape_;
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/// A operand storage
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using FragmentA = Array<half_t, Shape::kMK>;
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/// B operand storage
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using FragmentB = Array<half_t, Shape::kKN>;
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/// C operand storage
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using FragmentC = Array<half_t, Shape::kMN>;
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/// Underlying mathematical operator
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using Operator = arch::OpMultiplyAdd;
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static_assert(
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!(Shape::kN % 2),
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"Mma_HFMA2 requires the N dimension to be divisible by 2."
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);
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//
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// Methods
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//
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/// Computes a matrix product D = A * B + C
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CUTLASS_HOST_DEVICE
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void operator()(
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FragmentC & D,
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FragmentA const & A,
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FragmentB const & B,
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FragmentC const & C) {
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/// Initialize output with input
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D = C;
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/// Use 1x2x1 HFMA2 sequence for bulk of computation
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using Mma = arch::Mma<
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gemm::GemmShape<1,2,1>,
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1,
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half_t,
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layout::ColumnMajor,
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half_t,
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layout::ColumnMajor,
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half_t,
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layout::RowMajor,
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arch::OpMultiplyAdd>;
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Array<half_t, 2> *ptr_D = reinterpret_cast<Array<half_t, 2> *>(&D);
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Array<half_t, 1> const *ptr_A = reinterpret_cast<Array<half_t, 1> const *>(&A);
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Array<half_t, 2> const *ptr_B = reinterpret_cast<Array<half_t, 2> const *>(&B);
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Mma mma;
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CUTLASS_PRAGMA_UNROLL
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for(auto k=0; k < Shape::kK / Mma::Shape::kK; k++){
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CUTLASS_PRAGMA_UNROLL
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for(auto n=0; n < Shape::kN / Mma::Shape::kN; n++){
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CUTLASS_PRAGMA_UNROLL
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for(auto m=0; m < Shape::kM / Mma::Shape::kM; m++){
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Array<half_t, 2> tmp;
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Array<half_t, 2> *ptr_tmp = &tmp;
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ptr_tmp[0] = ptr_D[m*Shape::kN/2 + n];
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Array<half_t, 2> tmp_B;
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tmp_B[0] = ptr_B->at(2*n*Shape::kK + k);
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tmp_B[1] = ptr_B->at((2*n+1)*Shape::kK + k);
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mma(
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tmp,
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ptr_A[k*Shape::kM + m],
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tmp_B,
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tmp);
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ptr_D[m*Shape::kN/2 + n] = ptr_tmp[0];
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}
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}
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}
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}
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};
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/////////////////////////////
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// Specialization for NTN //
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/////////////////////////////
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template <typename Shape_>
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struct Mma_HFMA2 <
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Shape_,
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layout::ColumnMajor,
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layout::RowMajor,
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layout::ColumnMajor,
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true
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> {
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/// Size of the Gemm problem - concept: gemm::GemmShape<>
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using Shape = Shape_;
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/// A operand storage
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using FragmentA = Array<half_t, Shape::kMK>;
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/// B operand storage
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using FragmentB = Array<half_t, Shape::kKN>;
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/// C operand storage
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using FragmentC = Array<half_t, Shape::kMN>;
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/// Underlying mathematical operator
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using Operator = arch::OpMultiplyAdd;
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static_assert(
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!(Shape::kM % 2),
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"Mma_HFMA2 requires the GEMM M dimension to be divisible by 2."
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);
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//
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// Methods
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//
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/// Computes a matrix product D = A * B + C
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CUTLASS_HOST_DEVICE
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void operator()(
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FragmentC & D,
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FragmentA const & A,
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FragmentB const & B,
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FragmentC const & C) {
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/// Initialize output with input
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D = C;
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using Mma = arch::Mma<
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gemm::GemmShape<2,1,1>,
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1,
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half_t,
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layout::ColumnMajor,
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half_t,
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layout::RowMajor,
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half_t,
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layout::ColumnMajor,
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arch::OpMultiplyAdd>;
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Array<half_t, 2> *ptr_D = reinterpret_cast<Array<half_t, 2> *>(&D);
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Array<half_t, 2> const *ptr_A = reinterpret_cast<Array<half_t, 2> const *>(&A);
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Array<half_t, 1> const *ptr_B = reinterpret_cast<Array<half_t, 1> const *>(&B);
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Mma mma;
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CUTLASS_PRAGMA_UNROLL
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for (int k = 0; k < Shape::kK / Mma::Shape::kK; ++k) {
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CUTLASS_PRAGMA_UNROLL
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for (int m = 0; m < Shape::kM / Mma::Shape::kM; ++m) {
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CUTLASS_PRAGMA_UNROLL
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for (int n = 0; n < Shape::kN / Mma::Shape::kN; ++n) {
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Array<half_t, 2> tmp;
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Array<half_t, 2> *ptr_tmp = &tmp;
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ptr_tmp[0] = ptr_D[m + n * Shape::kM/2];
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mma(
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tmp,
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ptr_A[m + k * Shape::kM/2],
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ptr_B[k * Shape::kN + n],
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tmp);
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ptr_D[m + n * Shape::kM/2] = ptr_tmp[0];
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}
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}
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}
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}
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};
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/////////////////////////////
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// Specialization for NTT //
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/////////////////////////////
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template <typename Shape_>
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struct Mma_HFMA2<
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Shape_,
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layout::ColumnMajor,
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layout::RowMajor,
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layout::RowMajor,
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true
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> {
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/// Size of the Gemm problem - concept: gemm::GemmShape<>
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using Shape = Shape_;
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/// A operand storage
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using FragmentA = Array<half_t, Shape::kMK>;
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/// B operand storage
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using FragmentB = Array<half_t, Shape::kKN>;
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/// C operand storage
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using FragmentC = Array<half_t, Shape::kMN>;
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/// Underlying mathematical operator
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using Operator = arch::OpMultiplyAdd;
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static_assert(
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!(Shape::kN % 2),
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"Mma_HFMA2 requires the N dimension to be divisible by 2."
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);
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//
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// Methods
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//
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/// Computes a matrix product D = A * B + C
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CUTLASS_HOST_DEVICE
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void operator()(
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FragmentC & D,
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FragmentA const & A,
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FragmentB const & B,
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FragmentC const & C) {
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/// Initialize output with input
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D = C;
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/// Use 1x2x1 HFMA2 sequence for bulk of computation
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using Mma = arch::Mma<
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gemm::GemmShape<1,2,1>,
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1,
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half_t,
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layout::ColumnMajor,
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half_t,
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layout::RowMajor,
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half_t,
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layout::RowMajor,
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arch::OpMultiplyAdd>;
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Array<half_t, 2> *ptr_D = reinterpret_cast<Array<half_t, 2> *>(&D);
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Array<half_t, 1> const *ptr_A = reinterpret_cast<Array<half_t, 1> const *>(&A);
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Array<half_t, 2> const *ptr_B = reinterpret_cast<Array<half_t, 2> const *>(&B);
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Mma mma;
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CUTLASS_PRAGMA_UNROLL
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for(auto k=0; k < Shape::kK / Mma::Shape::kK; k++){
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CUTLASS_PRAGMA_UNROLL
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for(auto n=0; n < Shape::kN / Mma::Shape::kN; n++){
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CUTLASS_PRAGMA_UNROLL
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for(auto m=0; m < Shape::kM / Mma::Shape::kM; m++){
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Array<half_t, 2> tmp;
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Array<half_t, 2> *ptr_tmp = &tmp;
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ptr_tmp[0] = ptr_D[m*Shape::kN/2 + n];
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mma(
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tmp,
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ptr_A[k*Shape::kM + m],
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ptr_B[k*Shape::kN/2 + n],
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tmp);
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ptr_D[m*Shape::kN/2 + n] = ptr_tmp[0];
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}
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}
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}
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}
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};
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|
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/////////////////////////////
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// Specialization for TNN //
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/////////////////////////////
|
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|
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template <typename Shape_>
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struct Mma_HFMA2 <
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Shape_,
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layout::RowMajor,
|
|
layout::ColumnMajor,
|
|
layout::ColumnMajor,
|
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true
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> {
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|
|
|
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
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using Shape = Shape_;
|
|
|
|
/// A operand storage
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|
using FragmentA = Array<half_t, Shape::kMK>;
|
|
|
|
/// B operand storage
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|
using FragmentB = Array<half_t, Shape::kKN>;
|
|
|
|
/// C operand storage
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|
using FragmentC = Array<half_t, Shape::kMN>;
|
|
|
|
/// Underlying mathematical operator
|
|
using Operator = arch::OpMultiplyAdd;
|
|
|
|
static_assert(
|
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!(Shape::kM % 2),
|
|
"Mma_HFMA2 requires the M dimension to be divisible by 2."
|
|
);
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Computes a matrix product D = A * B + C
|
|
CUTLASS_HOST_DEVICE
|
|
void operator()(
|
|
FragmentC & D,
|
|
FragmentA const & A,
|
|
FragmentB const & B,
|
|
FragmentC const & C) {
|
|
|
|
/// Initialize output with input
|
|
D = C;
|
|
|
|
/// Use 1x1x1 HFMA2 sequence for bulk of computation
|
|
using Mma = arch::Mma<
|
|
gemm::GemmShape<2,1,1>,
|
|
1,
|
|
half_t,
|
|
layout::RowMajor,
|
|
half_t,
|
|
layout::ColumnMajor,
|
|
half_t,
|
|
layout::ColumnMajor,
|
|
arch::OpMultiplyAdd>;
|
|
|
|
Array<half_t, 2> *ptr_D = reinterpret_cast<Array<half_t, 2> *>(&D);
|
|
Array<half_t, 2> const *ptr_A = reinterpret_cast<Array<half_t, 2> const *>(&A);
|
|
Array<half_t, 1> const *ptr_B = reinterpret_cast<Array<half_t, 1> const *>(&B);
|
|
|
|
Mma mma;
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto k=0; k < Shape::kK / Mma::Shape::kK; k++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto m=0; m < Shape::kM / Mma::Shape::kM; m++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto n=0; n < Shape::kN / Mma::Shape::kN; n++){
|
|
|
|
Array<half_t, 2> tmp;
|
|
Array<half_t, 2> *ptr_tmp = &tmp;
|
|
ptr_tmp[0] = ptr_D[n*Shape::kM/2 + m];
|
|
|
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Array<half_t, 2> tmp_A;
|
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tmp_A[0] = ptr_A->at(2*m*Shape::kK + k);
|
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tmp_A[1] = ptr_A->at((2*m+1)*Shape::kK + k);
|
|
|
|
mma(
|
|
tmp,
|
|
tmp_A,
|
|
ptr_B[n*Shape::kK + k],
|
|
tmp);
|
|
|
|
ptr_D[n*Shape::kM/2 + m] = ptr_tmp[0];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
/////////////////////////////
|
|
// Specialization for TNT //
|
|
/////////////////////////////
|
|
|
|
template <typename Shape_>
|
|
struct Mma_HFMA2 <
|
|
Shape_,
|
|
layout::RowMajor,
|
|
layout::ColumnMajor,
|
|
layout::RowMajor,
|
|
true
|
|
> {
|
|
|
|
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
|
using Shape = Shape_;
|
|
|
|
/// A operand storage
|
|
using FragmentA = Array<half_t, Shape::kMK>;
|
|
|
|
/// B operand storage
|
|
using FragmentB = Array<half_t, Shape::kKN>;
|
|
|
|
/// C operand storage
|
|
using FragmentC = Array<half_t, Shape::kMN>;
|
|
|
|
/// Underlying mathematical operator
|
|
using Operator = arch::OpMultiplyAdd;
|
|
|
|
static_assert(
|
|
!(Shape::kN % 2),
|
|
"Mma_HFMA2 requires the N dimension to be divisible by 2."
|
|
);
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Computes a matrix product D = A * B + C
|
|
CUTLASS_HOST_DEVICE
|
|
void operator()(
|
|
FragmentC & D,
|
|
FragmentA const & A,
|
|
FragmentB const & B,
|
|
FragmentC const & C) {
|
|
|
|
/// Initialize output with input
|
|
D = C;
|
|
|
|
/// Use 1x2x1 HFMA2 sequence for bulk of computation
|
|
using Mma = arch::Mma<
|
|
gemm::GemmShape<1,2,1>,
|
|
1,
|
|
half_t,
|
|
layout::RowMajor,
|
|
half_t,
|
|
layout::ColumnMajor,
|
|
half_t,
|
|
layout::RowMajor,
|
|
arch::OpMultiplyAdd>;
|
|
|
|
Array<half_t, 2> *ptr_D = reinterpret_cast<Array<half_t, 2> *>(&D);
|
|
Array<half_t, 1> const *ptr_A = reinterpret_cast<Array<half_t, 1> const *>(&A);
|
|
Array<half_t, 2> const *ptr_B = reinterpret_cast<Array<half_t, 2> const *>(&B);
|
|
|
|
Mma mma;
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto k=0; k < Shape::kK / Mma::Shape::kK; k++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto n=0; n < Shape::kN / Mma::Shape::kN; n++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto m=0; m < Shape::kM / Mma::Shape::kM; m++){
|
|
|
|
Array<half_t, 2> tmp;
|
|
Array<half_t, 2> *ptr_tmp = &tmp;
|
|
ptr_tmp[0] = ptr_D[m*Shape::kN/2 + n];
|
|
|
|
Array<half_t, 2> tmp_B;
|
|
tmp_B[0] = ptr_B->at(2*n*Shape::kK + k);
|
|
tmp_B[1] = ptr_B->at((2*n+1)*Shape::kK + k);
|
|
|
|
mma(
|
|
tmp,
|
|
ptr_A[m*Shape::kK + k],
|
|
tmp_B,
|
|
tmp);
|
|
|
|
ptr_D[m*Shape::kN/2 + n] = ptr_tmp[0];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
/////////////////////////////
|
|
// Specialization for TTN //
|
|
/////////////////////////////
|
|
|
|
template <typename Shape_>
|
|
struct Mma_HFMA2 <
|
|
Shape_,
|
|
layout::RowMajor,
|
|
layout::RowMajor,
|
|
layout::ColumnMajor,
|
|
true
|
|
> {
|
|
|
|
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
|
using Shape = Shape_;
|
|
|
|
/// A operand storage
|
|
using FragmentA = Array<half_t, Shape::kMK>;
|
|
|
|
/// B operand storage
|
|
using FragmentB = Array<half_t, Shape::kKN>;
|
|
|
|
/// C operand storage
|
|
using FragmentC = Array<half_t, Shape::kMN>;
|
|
|
|
/// Underlying mathematical operator
|
|
using Operator = arch::OpMultiplyAdd;
|
|
|
|
static_assert(
|
|
!(Shape::kM % 2),
|
|
"Mma_HFMA2 requires the M dimension to be divisible by 2."
|
|
);
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Computes a matrix product D = A * B + C
|
|
CUTLASS_HOST_DEVICE
|
|
void operator()(
|
|
FragmentC & D,
|
|
FragmentA const & A,
|
|
FragmentB const & B,
|
|
FragmentC const & C) {
|
|
|
|
/// Initialize output with input
|
|
D = C;
|
|
|
|
/// Use 1x2x1 HFMA2 sequence for bulk of computation
|
|
using Mma = arch::Mma<
|
|
gemm::GemmShape<2,1,1>,
|
|
1,
|
|
half_t,
|
|
layout::RowMajor,
|
|
half_t,
|
|
layout::RowMajor,
|
|
half_t,
|
|
layout::ColumnMajor,
|
|
arch::OpMultiplyAdd>;
|
|
|
|
Array<half_t, 2> *ptr_D = reinterpret_cast<Array<half_t, 2> *>(&D);
|
|
Array<half_t, 2> const *ptr_A = reinterpret_cast<Array<half_t, 2> const *>(&A);
|
|
Array<half_t, 1> const *ptr_B = reinterpret_cast<Array<half_t, 1> const *>(&B);
|
|
|
|
Mma mma;
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto k=0; k < Shape::kK / Mma::Shape::kK; k++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto m=0; m < Shape::kM / Mma::Shape::kM; m++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto n=0; n < Shape::kN / Mma::Shape::kN; n++){
|
|
|
|
Array<half_t, 2> tmp;
|
|
Array<half_t, 2> *ptr_tmp = &tmp;
|
|
ptr_tmp[0] = ptr_D[n*Shape::kM/2 + m];
|
|
|
|
Array<half_t, 2> tmp_A;
|
|
tmp_A[0] = ptr_A->at(2*m*Shape::kK + k);
|
|
tmp_A[1] = ptr_A->at((2*m+1)*Shape::kK + k);
|
|
|
|
mma(
|
|
tmp,
|
|
tmp_A,
|
|
ptr_B[k*Shape::kN + n],
|
|
tmp);
|
|
|
|
ptr_D[n*Shape::kM/2 + m] = ptr_tmp[0];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
|
|
/////////////////////////////
|
|
// Specialization for TTT //
|
|
/////////////////////////////
|
|
|
|
template <typename Shape_>
|
|
struct Mma_HFMA2<
|
|
Shape_,
|
|
layout::RowMajor,
|
|
layout::RowMajor,
|
|
layout::RowMajor,
|
|
true
|
|
> {
|
|
|
|
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
|
using Shape = Shape_;
|
|
|
|
/// A operand storage
|
|
using FragmentA = Array<half_t, Shape::kMK>;
|
|
|
|
/// B operand storage
|
|
using FragmentB = Array<half_t, Shape::kKN>;
|
|
|
|
/// C operand storage
|
|
using FragmentC = Array<half_t, Shape::kMN>;
|
|
|
|
/// Underlying mathematical operator
|
|
using Operator = arch::OpMultiplyAdd;
|
|
|
|
static_assert(
|
|
!(Shape::kN % 2),
|
|
"Mma_HFMA2 requires the N dimension to be divisible by 2."
|
|
);
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Computes a matrix product D = A * B + C
|
|
CUTLASS_HOST_DEVICE
|
|
void operator()(
|
|
FragmentC & D,
|
|
FragmentA const & A,
|
|
FragmentB const & B,
|
|
FragmentC const & C) {
|
|
|
|
/// Initialize output with input
|
|
D = C;
|
|
|
|
/// Use 1x2x1 HFMA2 sequence for bulk of computation
|
|
using Mma = arch::Mma<
|
|
gemm::GemmShape<1,2,1>,
|
|
1,
|
|
half_t,
|
|
layout::RowMajor,
|
|
half_t,
|
|
layout::RowMajor,
|
|
half_t,
|
|
layout::RowMajor,
|
|
arch::OpMultiplyAdd>;
|
|
|
|
Array<half_t, 2> *ptr_D = reinterpret_cast<Array<half_t, 2> *>(&D);
|
|
Array<half_t, 1> const *ptr_A = reinterpret_cast<Array<half_t, 1> const *>(&A);
|
|
Array<half_t, 2> const *ptr_B = reinterpret_cast<Array<half_t, 2> const *>(&B);
|
|
|
|
Mma mma;
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto k=0; k < Shape::kK / Mma::Shape::kK; k++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto n=0; n < Shape::kN / Mma::Shape::kN; n++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto m=0; m < Shape::kM / Mma::Shape::kM; m++){
|
|
|
|
Array<half_t, 2> tmp;
|
|
Array<half_t, 2> *ptr_tmp = &tmp;
|
|
ptr_tmp[0] = ptr_D[m*Shape::kN/2 + n];
|
|
|
|
mma(
|
|
tmp,
|
|
ptr_A[m*Shape::kK + k],
|
|
ptr_B[k*Shape::kN/2 + n],
|
|
tmp);
|
|
|
|
ptr_D[m*Shape::kN/2 + n] = ptr_tmp[0];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////
|
|
// Specialization for TNT + Inner Product or 1x1x2K + LayoutC = T //
|
|
/////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename Shape_, typename LayoutA, typename LayoutB>
|
|
struct Mma_HFMA2<
|
|
Shape_,
|
|
LayoutA,
|
|
LayoutB,
|
|
layout::RowMajor,
|
|
false
|
|
> {
|
|
|
|
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
|
using Shape = Shape_;
|
|
|
|
/// A operand storage
|
|
using FragmentA = Array<half_t, Shape::kMK>;
|
|
|
|
/// B operand storage
|
|
using FragmentB = Array<half_t, Shape::kKN>;
|
|
|
|
/// C operand storage
|
|
using FragmentC = Array<half_t, Shape::kMN>;
|
|
|
|
/// Underlying mathematical operator
|
|
using Operator = arch::OpMultiplyAdd;
|
|
|
|
static_assert(
|
|
!(Shape::kK % 2),
|
|
"Mma_HFMA2 requires the K dimension to be divisible by 2."
|
|
);
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Computes a matrix product D = A * B + C
|
|
CUTLASS_HOST_DEVICE
|
|
void operator()(
|
|
FragmentC & D,
|
|
FragmentA const & A,
|
|
FragmentB const & B,
|
|
FragmentC const & C) {
|
|
|
|
/// Initialize output with input
|
|
D = C;
|
|
|
|
/// Use 1x1x2 HFMA2 sequence for bulk of computation
|
|
using GemmShape = gemm::GemmShape<1,1,2>;
|
|
|
|
Array<half_t, 1> *ptr_D = reinterpret_cast<Array<half_t, 1> *>(&D);
|
|
Array<half_t, 2> const *ptr_A = reinterpret_cast<Array<half_t, 2> const *>(&A);
|
|
Array<half_t, 2> const *ptr_B = reinterpret_cast<Array<half_t, 2> const *>(&B);
|
|
|
|
// Inner product is calculated using MACs, followed by final reduction
|
|
multiply_add<Array<half_t, 2>> mac;
|
|
cutlass::reduction::thread::Reduce< plus<half_t>, Array<half_t, 2> > reduce;
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto n=0; n < Shape::kN / GemmShape::kN; n++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto m=0; m < Shape::kM / GemmShape::kM; m++){
|
|
|
|
Array<half_t, 2> tmp_C;
|
|
tmp_C.clear();
|
|
Array<half_t, 1> *ptr_tmp_C = reinterpret_cast<Array<half_t, 1> *>(&tmp_C);
|
|
ptr_tmp_C[0] = ptr_D[n*Shape::kM + m];
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto k=0; k < Shape::kK / GemmShape::kK; k++){
|
|
tmp_C = mac(ptr_A[m*Shape::kK/2 + k], ptr_B[n*Shape::kK/2 + k], tmp_C);
|
|
}
|
|
|
|
Array<half_t, 1> res;
|
|
Array<half_t, 1> *ptr_res = &res;
|
|
res = reduce(tmp_C);
|
|
|
|
ptr_D[m*Shape::kN + n] = ptr_res[0];
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////
|
|
// Specialization for TNN + Inner Product or 1x1x2K + LayoutC = N //
|
|
/////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename Shape_, typename LayoutA, typename LayoutB>
|
|
struct Mma_HFMA2<
|
|
Shape_,
|
|
LayoutA,
|
|
LayoutB,
|
|
layout::ColumnMajor,
|
|
false
|
|
> {
|
|
|
|
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
|
using Shape = Shape_;
|
|
|
|
/// A operand storage
|
|
using FragmentA = Array<half_t, Shape::kMK>;
|
|
|
|
/// B operand storage
|
|
using FragmentB = Array<half_t, Shape::kKN>;
|
|
|
|
/// C operand storage
|
|
using FragmentC = Array<half_t, Shape::kMN>;
|
|
|
|
/// Underlying mathematical operator
|
|
using Operator = arch::OpMultiplyAdd;
|
|
|
|
static_assert(
|
|
!(Shape::kK % 2),
|
|
"Mma_HFMA2 requires the K dimension to be divisible by 2."
|
|
);
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Computes a matrix product D = A * B + C
|
|
CUTLASS_HOST_DEVICE
|
|
void operator()(
|
|
FragmentC & D,
|
|
FragmentA const & A,
|
|
FragmentB const & B,
|
|
FragmentC const & C) {
|
|
|
|
/// Initialize output with input
|
|
D = C;
|
|
|
|
/// Use 1x1x2 HFMA2 sequence for bulk of computation
|
|
using GemmShape= gemm::GemmShape<1,1,2>;
|
|
|
|
Array<half_t, 1> *ptr_D = reinterpret_cast<Array<half_t, 1> *>(&D);
|
|
Array<half_t, 2> const *ptr_A = reinterpret_cast<Array<half_t, 2> const *>(&A);
|
|
Array<half_t, 2> const *ptr_B = reinterpret_cast<Array<half_t, 2> const *>(&B);
|
|
|
|
// Inner product is calculated using MACs, followed by final reduction
|
|
multiply_add<Array<half_t, 2>> mac;
|
|
cutlass::reduction::thread::Reduce< plus<half_t>, Array<half_t, 2> > reduce;
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto n=0; n < Shape::kN / GemmShape::kN; n++){
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto m=0; m < Shape::kM / GemmShape::kM; m++){
|
|
|
|
Array<half_t, 2> tmp_C;
|
|
tmp_C.clear();
|
|
Array<half_t, 1> *ptr_tmp_C = reinterpret_cast<Array<half_t, 1> *>(&tmp_C);
|
|
ptr_tmp_C[0] = ptr_D[n*Shape::kM + m];
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for(auto k=0; k < Shape::kK / GemmShape::kK; k++){
|
|
|
|
tmp_C = mac(ptr_A[m*Shape::kK/2 + k], ptr_B[n*Shape::kK/2 + k], tmp_C);
|
|
|
|
}
|
|
|
|
Array<half_t, 1> res;
|
|
Array<half_t, 1> *ptr_res = &res;
|
|
res = reduce(tmp_C);
|
|
|
|
ptr_D[n*Shape::kM + m] = ptr_res[0];
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
} // namespace detail
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Structure to compute the matrix product
|
|
template <
|
|
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
|
typename Shape_, typename LayoutA, typename LayoutB, typename LayoutC
|
|
>
|
|
struct Mma<
|
|
Shape_,
|
|
half_t,
|
|
LayoutA,
|
|
half_t,
|
|
LayoutB,
|
|
half_t,
|
|
LayoutC,
|
|
arch::OpMultiplyAdd
|
|
> {
|
|
|
|
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
|
using Shape = Shape_;
|
|
|
|
/// Data type of operand A
|
|
using ElementA = half_t;
|
|
|
|
/// Data type of operand B
|
|
using ElementB = half_t;
|
|
|
|
/// Element type of operand C
|
|
using ElementC = half_t;
|
|
|
|
/// Underlying mathematical operator
|
|
using Operator = arch::OpMultiplyAdd;
|
|
|
|
/// A operand storage
|
|
using FragmentA = Array<ElementA, Shape::kMK>;
|
|
|
|
/// B operand storage
|
|
using FragmentB = Array<ElementB, Shape::kKN>;
|
|
|
|
/// C operand storage
|
|
using FragmentC = Array<ElementC, Shape::kMN>;
|
|
|
|
static bool const a_row_major = platform::is_same< LayoutA, layout::RowMajor>::value;
|
|
static bool const b_column_major = platform::is_same< LayoutB, layout::ColumnMajor>::value;
|
|
static bool const c_row_major = platform::is_same< LayoutC, layout::RowMajor>::value;
|
|
static bool const c_column_major = platform::is_same< LayoutC, layout::ColumnMajor>::value;
|
|
|
|
static bool const m_mod2 = !(Shape::kM % 2);
|
|
static bool const n_mod2 = !(Shape::kN % 2);
|
|
static bool const k_mod2 = !(Shape::kK % 2);
|
|
|
|
// HFMA based MMA optimizations are of 2 types :
|
|
// 1. Inner product
|
|
// 2. Outer product
|
|
// It is chosen based on LayoutC (for outer product gemm) or
|
|
// Using LayoutA and LayoutB or shape=1x1x2K (for inner product gemms)
|
|
// If all fails, we choose the generic MMA
|
|
static bool const use_outer_prod = (c_column_major && m_mod2) || (c_row_major && n_mod2);
|
|
static bool const use_inner_prod = (a_row_major && b_column_major && k_mod2) || (Shape::kM==1 && Shape::kN==1 && k_mod2);
|
|
static bool const use_optimized = (use_outer_prod || use_inner_prod);
|
|
|
|
using ArchMmaOperator = typename platform::conditional< use_optimized,
|
|
detail::Mma_HFMA2<Shape, LayoutA, LayoutB, LayoutC, use_outer_prod>,
|
|
MmaGeneric <Shape, ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, Operator>
|
|
>::type;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Computes a matrix product D = A * B + C
|
|
CUTLASS_HOST_DEVICE
|
|
void operator()(
|
|
FragmentC & D,
|
|
FragmentA const & A,
|
|
FragmentB const & B,
|
|
FragmentC const & C) {
|
|
|
|
ArchMmaOperator mma;
|
|
|
|
mma(D, A, B, C);
|
|
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace detail {
|
|
|
|
/// Determines whether to enable thread::Gemm<> specializations compatible with SM50
|
|
template <
|
|
typename LayoutA,
|
|
/// Layout of B matrix (concept: MatrixLayout)
|
|
typename LayoutB>
|
|
struct EnableMma_Crow_SM60 {
|
|
|
|
static bool const kIsConventionalLayout =
|
|
(platform::is_same<LayoutA, layout::RowMajor>::value ||
|
|
platform::is_same<LayoutA, layout::ColumnMajor>::value) &&
|
|
(platform::is_same<LayoutB, layout::RowMajor>::value ||
|
|
platform::is_same<LayoutB, layout::ColumnMajor>::value);
|
|
|
|
static bool const value = kIsConventionalLayout;
|
|
};
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Computes matrix product when C is row-major
|
|
template <
|
|
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
|
typename Shape_,
|
|
typename LayoutA_,
|
|
typename LayoutB_
|
|
>
|
|
struct Mma<
|
|
Shape_,
|
|
half_t,
|
|
LayoutA_,
|
|
half_t,
|
|
LayoutB_,
|
|
half_t,
|
|
layout::RowMajor,
|
|
arch::OpMultiplyAdd,
|
|
typename platform::enable_if<detail::EnableMma_Crow_SM60<
|
|
LayoutA_,
|
|
LayoutB_
|
|
>::value>::type>{
|
|
|
|
using Shape = Shape_;
|
|
using ElementA = half_t;
|
|
using LayoutA = LayoutA_;
|
|
using ElementB = half_t;
|
|
using LayoutB = LayoutB_;
|
|
using ElementC = half_t;
|
|
using LayoutC = layout::RowMajor;
|
|
using Operator = arch::OpMultiplyAdd;
|
|
|
|
using TransposeMma = Mma<
|
|
GemmShapeTranspose<Shape>,
|
|
half_t,
|
|
typename layout::LayoutTranspose<LayoutB>::type,
|
|
half_t,
|
|
typename layout::LayoutTranspose<LayoutA>::type,
|
|
half_t,
|
|
layout::ColumnMajor,
|
|
arch::OpMultiplyAdd,
|
|
bool>;
|
|
|
|
using FragmentA = Array<ElementA, Shape::kMK>;
|
|
using FragmentB = Array<ElementB, Shape::kKN>;
|
|
using FragmentC = Array<ElementC, Shape::kMN>;
|
|
|
|
using ArchMmaOperator = typename TransposeMma::ArchMmaOperator;
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
void operator()(
|
|
FragmentC & D,
|
|
FragmentA const & A,
|
|
FragmentB const & B,
|
|
FragmentC const & C) {
|
|
|
|
TransposeMma mma;
|
|
|
|
mma(D, B, A, C);
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace thread
|
|
} // namespace gemm
|
|
} // namespace cutlass
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|